Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion.

This paper proposes an incremental method for feature selection, aimed at identifying attributes in a dataset that allow to buid good classifiers at low computational cost. The basis of the approach is the minimal-redundancy-maximal-relevance (mRMR) framework, which attempts to select features relevant for a given classification task, avoiding redundancy among them. Relevance and redundancy have been popularly defined in terms of information theory concepts. In this paper a modification of the mRMR framework is proposed, based on a more proper quantification of the redundancy among features. Experimental work on discrete-valued datasets shows that classifiers built using features selected by the proposed method are more accurate than the ones obtained using original mRMR features. [ABSTRACT FROM AUTHOR]